All models are trained for 200 epochs following a training regime established by Cui et al. [1]}. Before final selection, all models and datasets are run on three different cuts of the training set. Since the variation in balanced accuracy was less than 2 points for all cuts, a single cut is selected for experimentation. The best performing model for each dataset and algorithm is selected for further investigation. Model performance is assessed using three metrics commonly used with imbalanced data: balanced accuracy (BAC), geometric mean (GM), the F1 measure (FM) [2]}.
